FAST Flux for low VRAM GPUs with Highest Quality. Installation, Tips & Performance Comparison.

Summary

The video explores FLUX models for high-quality image generation, addressing the challenge of running them on GPUs with limited VRAM. It introduces GGUF, a format for quantized models that significantly reduces size, making it suitable for low-end GPUs. The tutorial guides viewers through installing the GGUF loader on ComfyUI, integrating it with FLUX, and selecting appropriate quantized models. The video compares performance between original and quantized models, highlighting the benefits of GGUF for users with limited VRAM. It also touches on experimental features like Lora support and the impact of model choices on image generation outcomes.

Takeaways

• 🌐 The FLUX models are known for high-quality image generation but are also very large in size.
• 🔢 GGUF is a format for quantized models that significantly reduces their size, which is beneficial for GPUs with low VRAM.
• 📚 Meta's LLama uses GGUF, and now FLUX models are available in GGUF format on ComfyUI.
• 🚀 ComfyUI has an experimental Lora support and doesn't require the bits-and-bytes extension like NF4.
• 🔧 A video tutorial is provided for installing FLUX on ComfyUI and integrating the GGUF loader.
• 🔄 To install the GGUF extension, users need to update ComfyUI and may need to follow specific steps for different GPU types.
• 💾 Users should download the quantized FLUX models and place them in the ComfyUI models directory.
• 🔄 The script details the process of replacing the old diffusion loader with a new GGUF model loader node.
• 📸 The video demonstrates generating images with different models and resolutions to compare performance and quality.
• 📊 Performance tests show that quantized models are faster, especially at lower resolutions, but the difference lessens when excluding loading times.
• 🎨 The quality of generated images can vary between models, and changing the model or text encoder can influence the output.
• 🔄 Ongoing development of GGUF and FLUX models includes new versions and features like Lora support.

Q & A

What is the FLUX model mentioned in the script?

The FLUX model refers to a new type of model for image generation that offers extremely high quality but is also very large in size.

What is the issue with large FLUX models in terms of GPU usage?

Large FLUX models can be problematic for GPUs with low VRAM as they require substantial memory to run efficiently.

What is GGUF and how does it relate to FLUX models?

GGUF is a format for quantized models that significantly reduces their size. It's now available for FLUX models on ComfyUI, allowing for high performance even on GPUs with limited VRAM.

How does the GGUF format benefit users with low VRAM GPUs?

GGUF allows for the use of quantized models that are much smaller in size, thus requiring less VRAM, making it suitable for users with low VRAM GPUs.

What is the smallest quantized FLUX model size available in GGUF format?

The smallest quantized FLUX model available in GGUF format is just 4GB in size.

What is the benefit of using the GGUF loader in ComfyUI?

The GGUF loader in ComfyUI eliminates the need for the bits-and-bytes extension, which can be cumbersome, and also includes experimental Lora support.

How does one install the GGUF extension for ComfyUI?

To install the GGUF extension, one needs to update ComfyUI and then follow the instructions provided on the Comfy Anonymous GitHub page, which includes copying a command and running it in the installation directory.

What additional step is required for AMD GPU users with a ZLUDA environment?

AMD GPU users with a ZLUDA environment need to enter the base directory of their ZLUDA installation, backup the venv directory, and then run a specific command to install the GGUF extension.

How does one integrate the GGUF loader into their ComfyUI workflows?

To integrate the GGUF loader, one must replace the old diffusion loader with a new model loader node for GGUF in the ComfyUI examples for FLUX.

What is the performance difference between the original FLUX models and the quantized GGUF models?

The performance of quantized GGUF models is significantly faster, especially at lower resolutions, and they have faster loading times due to their smaller size. However, the performance difference evens out when ignoring loading times and at higher resolutions.

What are the key findings regarding the use of GGUF quantized models on low VRAM GPUs?

On low VRAM GPUs, the performance of GGUF quantized models increases significantly at lower resolutions. The FP8 model does not offer a performance advantage over the Q8O model, and the Q8O model often performs as well as or better than the original FP16 model.